Transitioning Clinical Practice Guidelines into the Electronic Health Record through Clinical Pathways

Author(s):  
Sharjeel M. Hooda ◽  
Karen K. Fields
2020 ◽  
Author(s):  
Sahnah Lim ◽  
Nadia S. Islam

UNSTRUCTURED Electronic health record quality improvement (QI) initiatives hold great promise in improving adoption of clinical practice guidelines, including those related to diabetes. QI initiatives implemented in under-resourced primary care settings that primarily serve racial/ethnic minority populations have potential to improve quality of care and ultimately improve diabetes disparities. The “Screen at 23” campaign was launched in 2011 to increase screening for prediabetes and diabetes at lower body mass index (BMI) thresholds (i.e., 23 kg/m2) for Asian Americans, in line with the new guidelines put forth by the American Diabetes Association. Here, we describe the implementation of a customized electronic health record QI initiative in under-resourced practices that primarily serve low-income South Asian populations in New York City, designed to increase diabetes screening using updated BMI guidelines and in alignment with the “Screen at 23” campaign. The customization involved the implementation of an innovative, semi-manual alternate solution to automated clinical decision support systems (CDSS) alerts in order to address the restrictions on customizing CDSS alerts in electronic health record platforms used in small practice settings. We also discuss challenges and strategies with this customized QI effort. Our experience suggests that multi-sector partnership engagement, user-centered approaches, and relationship-building with key stakeholders are even more critical in under-resourced, and small practice settings. Relatively simple technological solutions can be greatly beneficial in enhancing small practice capacity to engage in larger-scale QI initiatives. Tailored, context-driven approaches for implementation of equity-focused QI initiatives such as the one we describe can increase adoption of clinical practice guidelines, improve diabetes-related outcomes, and improve health disparities among under-served populations. INTERNATIONAL REGISTERED REPORT RR2-https://doi.org/10.1186/s13063-019-3711-y


2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 324-324
Author(s):  
Isaac S. Chua ◽  
Elise Tarbi ◽  
Jocelyn H. Siegel ◽  
Kate Sciacca ◽  
Anne Kwok ◽  
...  

324 Background: Delivering goal-concordant care to patients with advanced cancer requires identifying eligible patients who would benefit from goals of care (GOC) conversations; training clinicians how to have these conversations; conducting conversations in a timely manner; and documenting GOC conversations that can be readily accessed by care teams. We used an existing, locally developed electronic cancer care clinical pathways system to guide oncologists toward these conversations. Methods: To identify eligible patients, pathways directors from 12 oncology disease centers identified therapeutic decision nodes for each pathway that corresponded to a predicted life expectancy of ≤1 year. When oncologists selected one of these pre-identified pathways nodes, the decision was captured in a relational database. From these patients, we sought evidence of GOC documentation within the electronic health record by extracting coded data from the advance care planning (ACP) module—a designated area within the electronic health record for clinicians to document GOC conversations. We also used rule-based natural language processing (NLP) to capture free text GOC documentation within these same patients’ progress notes. A domain expert reviewed all progress notes identified by NLP to confirm the presence of GOC documentation. Results: In a pilot sample obtained between March 20 and September 25, 2020, we identified a total of 21 pathway nodes conveying a poor prognosis, which represented 91 unique patients with advanced cancer. Among these patients, the mean age was 62 (SD 13.8) years old; 55 (60.4%) patients were female, and 69 (75.8%) were non-Hispanic White. The cancers most represented were thoracic (32 [35.2%]), breast (31 [34.1%]), and head and neck (13 [14.3%]). Within the 3 months leading up to the pathways decision date, a total 62 (68.1%) patients had any GOC documentation. Twenty-one (23.1%) patients had documentation in both the ACP module and NLP-identified progress notes; 5 (5.5%) had documentation in the ACP module only; and 36 (39.6%) had documentation in progress notes only. Twenty-two unique clinicians utilized the ACP module, of which 1 (4.5%) was an oncologist and 21 (95.5%) were palliative care clinicians. Conclusions: Approximately two thirds of patients had any GOC documentation. A total of 26 (28.6%) patients had any GOC documentation in the ACP module, and only 1 oncologist documented using the ACP module, where care teams can most easily retrieve GOC information. These findings provide an important baseline for future quality improvement efforts (e.g., implementing serious illness communications training, increasing support around ACP module utilization, and incorporating behavioral nudges) to enhance oncologists’ ability to conduct and to document timely, high quality GOC conversations.


2019 ◽  
Vol 37 (27_suppl) ◽  
pp. 322-322
Author(s):  
Lauren E. Geisel ◽  
Helen M. Johnson ◽  
Andrew Weil ◽  
Mahvish Muzaffar ◽  
Nasreen A. Vohra ◽  
...  

322 Background: Clinical pathways are widely accepted tools for improving the quality of cancer care. We developed and implemented, within the electronic health record (EHR), a standardized multidisciplinary breast cancer conference template comprised of NCCN clinical pathway elements, with triggers to promote adherence and measure compliance. Methods: The records of breast cancer patients diagnosed from January 2016 to December 2017 were reviewed. Baseline data on (1) the documentation of clinical stage prior to prospective presentation at multidisciplinary conference, (2) documentation of family history, and (3) functional breast imaging utilization were recorded. EHR enhancements developed throughout 2018 were implemented in January 2019. Post-implementation data were obtained via an EHR query of records from January 2019 to the present. Results: At baseline, 56.5% of new patients (n = 435) had a clinical stage documented appropriately (goal 100%). After the EHR enhancements went live, this rate increased to 76.9% (n = 78 new diagnoses), ranging from 40% for patients with metastatic disease to 85.7% for non-metastatic. Compared with baseline data, EHR-derived data from 149 multidisciplinary conference notes demonstrated relatively stable rates of compliance with the family history and imaging metrics: 94.3% to 93.9% (goal 100%), and 12.8% to 13.4% (goal ≤20%), respectively. In 2019, there were 128 instances of an EHR trigger prompting physicians to review the multidisciplinary conference recommendations. While 89.1% of users responded that they reviewed the note, only 42.1% of these clicked on the link to view it. Conclusions: The EHR is a powerful tool for incorporating clinical pathways into oncology providers’ daily workflow. Quality improvement data can be extracted rapidly and efficiently, which facilitates continuous QI. We observed a notable improvement in documentation of clinical staging prior to multidisciplinary conference after the implementation of the clinical pathways in the EHR. Our first report identified several areas for improvement, which will be the focus of subsequent PDSA cycles.


Author(s):  
Mouna Berquedich ◽  
Oulaid Kamach ◽  
Malek Masmoudi ◽  
Laurent Deshayes

Clinical pathways indicate the applicable treatment order of interventions. In this paper we propose a data-driven methodology to extract common clinical pathways from patient-centric Electronic Health Record data (EHR). The analysis of  patient's, can lead to better regarding pathologies. The proposed algorithmic methodology consist to designing a system of control and analysis of patient records based on an analogy between the elements of the new EHRs and the biological immune systems. The detection of patient profiles ensured by biclustering Matrix. We rely on biological immunity to develop a set of models for structuring knowledge extracted from EHR and to make pathway analysis decisions. A specific analysis of the functional data leds to the detection of several types of patients who share the same EHR information. This methodology demonstrates its ability to simultaneously processing data, and is able to providing information for understanding and identifying the path of patients as well as predicting the path of future patients.


2017 ◽  
pp. 1307-1323
Author(s):  
Yiye Zhang ◽  
Rema Padman

This chapter discusses clinical practice guidelines (CPGs) and their incorporation into healthcare IT (HIT) applications. CPGs provide guidance on treatment options based on evidence. This chapter provides a brief background on challenges in CPG development and adherence, and offers examples of data-driven approaches to improve usability of CPGs and their applications in HIT. A focus is given to clinical pathways, which translate CPG recommendations into actionable plans for patient management in community practices. Approaches for developing data-driven clinical pathways from electronic health record data are presented, including statistical, process mining, and machine learning algorithms. Further, efforts on using CPGs for decision support through visual analytics, and deployments of CPGs into mobile applications are described. Data-driven approaches can facilitate incorporation of practice-based evidence into CPG development after validation by clinical experts, potentially bridging the gap between available CPGs and changing clinical needs and workflow management.


Author(s):  
Yiye Zhang ◽  
Rema Padman

This chapter discusses clinical practice guidelines (CPGs) and their incorporation into healthcare IT (HIT) applications. CPGs provide guidance on treatment options based on evidence. This chapter provides a brief background on challenges in CPG development and adherence, and offers examples of data-driven approaches to improve usability of CPGs and their applications in HIT. A focus is given to clinical pathways, which translate CPG recommendations into actionable plans for patient management in community practices. Approaches for developing data-driven clinical pathways from electronic health record data are presented, including statistical, process mining, and machine learning algorithms. Further, efforts on using CPGs for decision support through visual analytics, and deployments of CPGs into mobile applications are described. Data-driven approaches can facilitate incorporation of practice-based evidence into CPG development after validation by clinical experts, potentially bridging the gap between available CPGs and changing clinical needs and workflow management.


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